Honest figures: Ethical data professionals in biology

BIOSCI 738 @ Waipapa Taumata Rau

Liza Bolton (guest lecture)

2025-03-17

Scan for slides & runsheet

or go to link.lizabolton.com

Kia ora koutou 欢迎大家

You hate your friend’s new haircut

If they ask you what you think, what do you say?




Google (2025) Gemini AI generated image from prompt: Create a lifelike image of 21-year-old person with a really terrible broccoli haircut. Truly disastrous, please. The hair should be green.

Why be ethical?

  • You want to do the ‘right’ thing
  • You / your organisation want to avoid negative professional consequences and reputational risks (have positive ones)
  • You / your organisation wants to avoid legal risks and comply with regulation

Some questions for ethical decision-making

The following are adapted from the Waipapa Taumata Rau Science Course WTRSCI100

  1. “Does this action reflect the character traits that I strive to embody?” (virtue ethics)

  2. “Will the overall consequences of my action result in the greatest good for the greatest number of people?” (consequentialism)

  3. “Will my action positively affect the strength of interconnectedness and respect the mutual responsibilities inherent in the relationships that I have with others?” (relational ethics)

  4. “Does this action satisfy my moral responsibilities?” (deontology)

Codes of conduct

Last week you developed a Code of Conduct for this course. You may have also seen the Waipapa Taumata Rau University of Auckland Code of Conduct that applies to you as a student here.

The purpose of this Code is to develop and maintain a standard of behaviour that supports and enables the University’s commitment to being a safe, inclusive, equitable and respectful community; both in-person and online.


For people working in research and academia there are usually professional societies that have developed codes of conduct and ethical standards. The Royal Society Te Apārangi is a long established and well respected not-for-profit organisation in Aotearoa New Zealand and their Code of Conduct.


Link to poster summary

Group discussion 1 — see the runsheet

☕️ 10 minute break

Exploring a few ‘big ideas’ for ethical data practices

Reproducibility, transperancy, open data

  • Making data and code available (freely online, or ‘upon application to the author’)
  • Approaching you code writing in reproducible ways
  • Making it possible for others to replicate your methods and analyses

Indigenous data sovereignty

  • Module 1 in your coursebook includes notes about data sovereignty, especially principles around Indigenous Data Sovereignty.
  • Some advice and concepts that are commonly promoted in open data and transparency movements are directly counter to sovereignty over data.
    • You must consider what the relevance and appropriate balance of these principles is for the given research situation.

Optional video: Data Democratisation Panel from the Science Communicators Association of New Zealand

Link

The dead fish that can read human emotions



Story time Poster link







📚 Read more in this Scientific American article about the IgNobel prize this study won.

Researcher degrees of freedom

  • Your decisions matter!
  • You may be familiar with ‘degrees of freedom’ from your previous statistics courses. This value tells us something about how many independent pieces of information go into our estimation of the parameter we’re interested in.
  • Researcher degrees of freedom are a way of discussing the flexibility and range of ways we might approach collecting and measuring data.



📚 Recommended reading: Wicherts JM, Veldkamp CL, Augusteijn HE, Bakker M, van Aert RC, van Assen MA. Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Front Psychol. 2016 Nov 25;7:1832. doi: 10.3389/fpsyg.2016.01832. PMID: 27933012; PMCID: PMC5122713.

Communication

Doing the work is not enough — you need to make sure that communication to stakeholders about your work and results is done accurately and honestly.

  • Appropriate data visualisations
  • Use clear and plain language descriptions when appropriate
  • Don’t leave things out
  • Be honest about limitations of your work
  • Reference appropriately

What you might have to do sometimes (the not fun stuff)

  • 📣‘Blow the whistle’
    • Well run organisations will have ways to ‘whistle blow’ and make complaints, as will some professional societies. In some (rare) cases, going to law enforcement or the media may be needed.
  • 💸 Refuse funding from sources that could compromise your integrity.
  • ✊🏽 Stand up to people in authority, who you might respect or have looked up to.

Group discussion 2: case studies — see the runsheet

Wrap-up

Thanks for having me!

📧 liza.bolton@auckland.ac.nz

Slides: link.lizabolton.com